The most accurate company lookalike API
Short answer, from an independent test on identical seed companies: OpenFunnel leads on long-list relevance (Precision@100). PredictLeads leads on top-of-list precision (Precision@10). "Most accurate" isn't one number — it depends on whether you act on the first ~10 results or the first ~100 — so this benchmark reports both, on the same inputs, fully reproducible.
Company lookalike APIs, ranked by relevance
Every provider gets the same seed companies and the same input; an LLM judge scores how many of the companies each returns are actually relevant. Precision@100 is the headline accuracy number — of the 100 companies you paid for, how many are usable. Ranked highest-first:
| # | Provider | Precision@100 (long-list) | Precision@10 (top-of-list) | Cost / relevant | Agent-ready |
|---|---|---|---|---|---|
| 1 | OpenFunnel | 69.8% | 77.9% | — | yes |
| 2 | Parallel | 56.5% | 72.1% | — | yes |
| 3 | Ocean.io | 48.6% | 69.6% | — | no |
| 4 | Exa | 25.8% | 77.9% | <$0.01 | yes |
| 5 | PredictLeads | 19.4% | 93.8% | — | no |
Numbers are point-in-time against a specific dataset and refresh as seeds are added — they don't generalize indefinitely. Every cell is reproducible from the raw request/response and judge prompt. See the full per-seed matrix and methodology →
"Most accurate" depends on how you use the list
A provider can win the top-10 and collapse over the full 100, or hold relevance deep but never top the first handful. Pick by the axis that matches your workflow:
| If you care about… | The axis | Current leader |
|---|---|---|
| Acting on a short, hand-checked list | Top-of-list precision (Precision@10) | PredictLeads |
| Building a large target list / TAM | Long-list relevance (Precision@100) | OpenFunnel |
| Cost discipline at scale | Cost per relevant company | OpenFunnel |
Picking one for a closed-won lookalike play
If the goal is "find more companies like the deals we already won," the most accurate output depends more on your seed than on the vendor. Three things you control, in order of impact:
| What to do | Why it moves accuracy |
|---|---|
| Clean the seed | Don't feed raw closed-won. Drop one-off wins, churned-fast accounts, and deals that closed for reasons that won't generalize. A clean seed of your best, expansion-friendly, fast-closing wins beats a bigger messy one — garbage seed in, garbage lookalikes out. |
| Go beyond firmographics | Industry + size + revenue lookalikes amplify whatever bias is already in your seed. The accuracy lift comes from layering tech-stack, growth, and intent signals on top of firmographic similarity. |
| Close the loop | Whichever provider you pick, score its output against actual conversion and re-tune your definition of "lookalike." Static similarity scores decay as your ICP shifts. |
The honest test: run the same closed-won seed through the top two ranked providers and eyeball the overlap and the obvious misses in your vertical. That 30-minute check tells you more than any single headline number — and you can start from the ranking above instead of guessing. Compare all five on the live benchmark →